ED15-0647
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INTRODUCTION
Length of productive life (LPL) and lifetime production
traits (lifetime number of piglets born alive [LBA], lifetime
number of piglets weaned [LPW], lifetime litter birth
weight [LBW], and lifetime litter weaning weight [LWW])
are important for commercial swine operations because they
affect efficiency of production, costs, and profitability.
Sows that have long LPL would be more productive and
profitable than sows with short LPL (Stalder et al., 2003;
Abell, 2011). Highly productive sows are preferred by
commercial swine producers and kept for as long as
possible in the production system. Further, sows with longer
LPL are likely to be healthier (Tummaruk et al., 2001) and
their offspring better defended against infectious
microorganisms because they receive higher levels of
antibodies from their dams than progeny from sows with
shorter LPL (Sobczyńska et al., 2013). Thus, increasing
LPL is expected to increase the productivity of sows in the
breeding herd as well as the profitability of swine
operations. However, swine producers in Thailand have
focused their culling and selection on traits measured in
individual parities (e.g., litter size at birth and weaning,
individual piglet weight and litter weight at birth and at
weaning) instead of LPL and lifetime production traits. The
target of Thai commercial swine producers has been to
produce more and heavier piglets to reduce production costs
Open Access
Asian Australas. J. Anim. Sci. Vol. 00, No. 00 : 0000-0000 Month 0000
http://dx.doi.org/10.5713/ajas.15.0647
www.ajas.info pISSN 1011-2367 eISSN 1976-5517
Estimation of Genetic Parameters and Trends for Length of
Productive Life and Lifetime Production Traits in a
Commercial Landrace and Yorkshire Swine Population in Northern Thailand
Udomsak Noppibool, Mauricio A. Elzo1, Skorn Koonawootrittriron*, and Thanathip Suwanasopee
Department of Animal Science, Faculty of Agriculture, Kasetsart University, Bangkok 10900, Thailand
ABSTRACT: The objective of this research was to estimate genetic parameters and trends for length of productive life (LPL), lifetime
number of piglets born alive (LBA), lifetime number of piglets weaned (LPW), lifetime litter birth weight (LBW), and lifetime litter
weaning weight (LWW) in a commercial swine farm in Northern Thailand. Data were gathered during a 24-year period from July 1989
to August 2013. A total of 3,109 phenotypic records from 2,271 Landrace (L) and 838 Yorkshire sows (Y) were analyzed. Variance and
covariance components, heritabilities and correlations were estimated using an Average Information Restricted Maximum Likelihood
(AIREML) procedure. The 5-trait animal model contained the fixed effects of first farrowing year-season, breed group, and age at first
farrowing. Random effects were sow and residual. Estimates of heritabilities were medium for all five traits (0.17±0.04 for LPL and
LBA to 0.20±0.04 for LPW). Genetic correlations among these traits were high, positive, and favorable (p<0.05), ranging from
0.93±0.02 (LPL-LWW) to 0.99±0.02 (LPL-LPW). Sow genetic trends were non-significant for LPL and all lifetime production traits.
Sire genetic trends were negative and significant for LPL (–2.54±0.65 d/yr; p = 0.0007), LBA (–0.12±0.04 piglets/yr; p = 0.0073), LPW (–0.14±0.04 piglets/yr; p = 0.0037), LBW (–0.13±0.06 kg/yr; p = 0.0487), and LWW (–0.69±0.31 kg/yr; p = 0.0365). Dam genetic
trends were positive, small and significant for all traits (1.04±0.42 d/yr for LPL, p = 0.0217; 0.16±0.03 piglets/yr for LBA, p<0.0001;
0.12±0.03 piglets/yr for LPW, p = 0.0002; 0.29±0.04 kg/yr for LBW, p<0.0001 and 1.23±0.19 kg/yr for LWW, p<0.0001). Thus, the
selection program in this commercial herd managed to improve both LPL and lifetime productive traits in sires and dams. It was
ineffective to improve LPL and lifetime productive traits in sows. (Key Words: Genetic Parameters, Length of Productive Life, Lifetime
Production Traits, Swine, Tropics)
Copyright © 2016 by Asian-Australasian Journal of Animal Sciences This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/),
which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
* Corresponding Author: Skorn Koonawootrittriron. Tel: +66-2-
5791120, Fax: +66-2-5791120, E-mail: [email protected] 1 Department of Animal Sciences, University of Florida,
Gainesville, FL 32611-0910, USA.
Submitted Aug. 1, 2015; Revised Nov. 23, 2015; Accepted Dec. 23, 2015
Noppibool et al. (0000) Asian Australas. J. Anim. Sci. 00:0000-0000
ED15-0647
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and maximize profits.
Most Thai swine producers use an open-house system to
keep animals throughout the year. Such system exposes
sows to more variation under tropical environmental
conditions than sows kept in climate-controlled barns.
However, Thai swine farms utilize European breeds such as
Landrace and Yorkshire originated under temperate climate
conditions. Thus, to establish a genetic improvement
program for LPL and lifetime production traits in Thailand,
genetic parameters for these traits under open-house
tropical production conditions are needed. Only a single
unpublished study on genetic parameters for LPL, LBA,
and LPW exists in Thailand. Keonouchanh (2002) reported
low heritability for LPL (0.03 to 0.04), LBA (0.18 to 0.20),
and LPW (0.12 to 0.19) and low and positive genetic
correlations between LPL and LBA (0.25 to 0.65) in a
swine population composed of Duroc, Landrace, and Large
White in Northeastern Thailand. Thus, a study involving
LPL and lifetime numbers and weights of piglets at birth
and at weaning is needed to develop comprehensive swine
genetic improvement programs for LPL and lifetime
production traits in Thailand. Consequently, the objective of
this research were to estimate genetic parameters and trends
for LPL, LBA, LPW, LBW, and LWW using data from a
commercial swine population composed of purebred
Landrace and Yorkshire pigs kept in an open-house system
under Thai tropical environmental conditions.
MATERIALS AND METHODS
Data, animals and traits
This research utilized a field dataset from a commercial
swine farm in Northern Thailand. The original dataset
included 3,541 Landrace (L) and Yorkshire (Y) sows. Sow
records consisted of sow identification, sow breed, sire
breed, dam breed, parity number, sow birth date, farrowing
date, number of piglets born alive, number of piglets
weaned, weight at birth and weight at weaning for each
parity. Parity of sows was classified into 1, 2, 3, 4, 5, 6, 7, 8,
9, and 10 and more parities. Only sows that farrowed
continuously, had at least one lifetime production trait, and
had completed their lifetime production (i.e., known date of
first farrowing and date of last weaning) were kept for the
study. Sows that were still alive, had missing records, or
had their first farrowing at less than 300 or more than 500 d
of age were excluded. After the editing process, 3,109 sows
(2,271 L and 838 Y) with complete lifetime records in the
breeding herd, and had their first farrowing between July
1989 and August 2013 were included in the study.
The LPL was defined as the number of days between
age of sow at first farrowing and age of sow at weaning of
her last farrowing. The LBA was the number of piglets born
alive during the lifetime of a sow. The LPW was the
number of piglets weaned over the lifetime of a sow. The
LBW was the sum of the birth weights of all piglets born
during the lifetime of a sow. The LWW was the sum of the
weaning weights of all piglets weaned during the lifetime of
a sow.
Climate, nutrition and management
The swine commercial herd used in this research was
located in the province of Chiang Mai, Northern Thailand
(18° 47′ 43″ latitude North and 98° 59′ 55″ longitude East;
elevation = 310 m above sea level). The average
temperature in this area was 27°C (average minimum =
17°C; average maximum = 34.5°C), the average rainfall
was 1,218 mm (average minimum = 880 mm; average
maximum = 1,457 mm), and the average humidity was
73.2% (average minimum = 37%; average maximum = 99%)
over the last thirteen years (Thai Meteorological
Department, 2014). Seasons were defined as winter
(November to February), summer (March to June), and
rainy (July to October). All gilts and sows were kept in an
open-house system. Gilts and sows that had their first litter
in the same year-season received similar feeding and
management. Gilts and non-lactating sows received 2.5
kg/d of feed with 16% crude protein and 3,200 to 3,500
kcal/kg (two feeding times; 07:00 am and 13:00 pm),
whereas nursing sows received 5 to 6 kg/d of feed with 17%
to 18% crude protein and 4,060 kcal/kg (four feeding times;
07:00 am, 10:00 am, 13:00 pm and 15:00 pm).
Mating was performed by artificial insemination. Estrus
was detected by visual appraisal (reddening and swelling of
the vulva) and by boar exposure twice a day (morning and
afternoon). Replacement gilts were inseminated in their
third observed estrus (8 to 9 mo of age and body weight of
at least 140 kg). Sows were serviced on the second
observed estrus (twice; firstly 12 h after detecting estrus and
then 12 h later). Gilts and sows were kept in individual
stalls in open-house buildings with dripping, fogging, and
fans placed in the farrowing unit approximately 7 d before
farrowing. Piglets were weaned when they reached 5 to 7
kg of body weight or 26 to 30 d of age.
Environmental and genetic fixed effects
Descriptive statistics for LPL, LBA, LPW, LBW, and
LWW were obtained with the MEAN procedure of SAS
(SAS, 2004). The general linear model procedure of SAS
was used to assess the importance of fixed effects on all
traits using in single-trait fixed models. The single-trait
fixed models for LPL, LBA, LPW, LBW, and LWW
contained the effects of first farrowing year-season (73
year-season combinations), breed group (L and Y), and age
at first farrowing (10 to 17 mo). Least squares means (LSM)
were estimated for all first farrowing year-season and breed
group subclasses. Comparison between LSM was done
Noppibool et al. (0000) Asian Australas. J. Anim. Sci. 00:0000-0000
ED15-0647
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using Bonferroni t-tests.
Variance components and genetic parameters
A five-trait analysis was carried out to estimate variance
and covariance components using an Average Information
Restricted Maximum Likelihood (AI-REML) algorithm
(ASREML; Gilmour et al., 2000). Estimates of variance
components were subsequently used to calculate
heritabilities, genetic correlations, and phenotypic
correlations between LPL and lifetime production traits
(LBA, LPW, LBW, and LWW). The 5-trait mixed animal
model contained the fixed effects of first farrowing year-
season and breed group (L and Y) as subclass fixed effects,
and age at first farrowing as a fixed covariate. Random
effects were sow and residual. The pedigree file contained
5,525 animals, 690 sires, and 1,512 dams. The genetic
parameters were estimated using the following animal
model:
𝑦 = 𝑋𝑏 + ZaQag
a+ Zag
a+ 𝑒
where y is the vector of sow records for LPL, LBA,
LPW, LBW, and LWW, b is the vector of contemporary
groups (first farrowing year-season subclasses) and a
covariate for age at first farrowing (mo), ga is vector of
additive group genetic effects (L and Y), aa is the vector of
random animal additive genetic effects deviated from their
breed group, X is an incidence matrix relating sow records
to fixed effects in vector b, Za is an incidence matrix
relating sow records to random animal additive genetic
effects in vector aa, Qa is an incidence matrix relating
elements of vector aa to additive genetic groups in vector ga
and e is the vector of residual random effects. The
assumptions of the model were:
[y
aa
e] ~ MVN ([
𝑋𝑏 + ZaQag
a
0
0
] , [ZaGaZ'a+ R
GaZ'aR
ZaGa
Ga
0
R
0
R
])
where Ga = 𝐺0⨂𝐴, where G0 is a 5×5 matrix of genetic
covariances among LPL, LBA, LPW, LBW, and LWW, A is
the numerator relationships matrix, ⊗ represents direct
product, and R = 𝑅0 ⨂ 𝐼 , where 𝑅0 is a 5×5 matrix of
residual covariances among LPL, LBA, LPW, LBW, and
LWW, and I is an identity matrix. The estimated variance
and covariance components were used to compute genetic
parameters for all traits (heritabilities, genetic correlations,
and phenotypic correlations).
Additive genetic predictions and genetic trends
Additive genetic predictions were computed for all sows,
sires, and dams in the population using the 5-trait animal
model described above and estimates of variance
components values obtained at convergence. The estimated
breeding value for each animal was computed as the sum of
breed group solution and its predicted additive genetic
effect deviated from its breed group. Because breed effects
are not estimable but breed differences are estimable, breed
effects were estimated as deviations from Landrace for all
traits. Weighted EBV means for sows, sires and dams were
computed for all traits at each first-farrowing year (FFY;
1989 to 2013), where weights were the number of litters per
year for sows, sires, and dams. Weighted yearly means for
sow, sire, and dam EBV were plotted against FFY to
illustrate changes in mean EBV for these animals during the
years of the study. Genetic trends for sow, sire, and dam
EBV from 1989 to 2013 were computed as linear regression
coefficients of mean sow, sire, and dam EBV on FFY with
the REG procedure of SAS (SAS, 2004).
RESULTS AND DISCUSSION
Environmental and genetic fixed effects
Means, standard deviation, minimum, and maximum
values for LPL, LBA, LPW, LBW, and LWW are shown in
Table 1. The LPL ranged from 21 to 1,596 d, with an
average of 680 d. The average for lifetime production traits
were 52 piglets for LBA, 46 piglets for LPW, 82 kg for
LBW and 337 kg for LWW. First farrowing year-season,
breed group, and age at first farrowing affected all traits
(p<0.0373 to p<0.0001; Table 2).
The LSM for FFY-seasons ranged from 281.39±141.74
(2013-rainy) to 1,036.88±99.81 (1993-rainy) d for LPL,
27.40±10.11 (2013-rainy) to 70.34±8.56 (1996-rainy)
piglets for LBA, 21.01±8.87 (2013-rainy) to 63.90±7.50
(1996-rainy) piglets for LPW, 43.77±13.44 (2001-summer)
to 109.95±5.92 (2009-rainy) kg for LBW, and 159.71±66.92
(2013-rainy) to 469.67±25.01 (2009-rainy) kg for LWW.
These ranges clearly show that FFY-seasons had large
effects on all traits in this population. Variation in
management strategies, quality and quantity of feed, as well
as variability in climate conditions during the years of the
study may largely account for these wide ranges of LSM
values.
Gilts that had their first farrowing at younger ages had
Table 1. Descriptive statistics for length of productive life and
lifetime production traits
Traits No. Mean SD Minimum Maximum
LPL 3,066 680.35 420.53 21.00 1,596.00
LBA 3,068 52.08 29.37 1.00 116.00
LPW 3,016 45.72 26.05 1.00 100.00
LBW 3,000 82.22 45.88 1.00 175.00
LWW 2,994 337.06 196.69 6.00 790.00
SD, standard deviation; LPL, length of productive life; LBA, lifetime
number of piglets born alive; LPW, lifetime number of piglets weaned;
LBW, lifetime litter birth weight; LWW, lifetime litter weaning weight.
Noppibool et al. (0000) Asian Australas. J. Anim. Sci. 00:0000-0000
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significantly longer LPL (–34.54±7.25 d/mo; p<0.0001),
higher LBA (–2.20±0.52 piglets/mo; p<0.0001), higher
LPW (–1.85±0.46 piglets/mo; p<0.0001), heavier LBW (–
2.72±0.82 kg/mo; p = 0.0009) and heavier LWW (–
11.82±3.49 kg/mo; p = 0.0007) than gilts that had their first
farrowing at older ages. These findings were in agreement
with results from other authors (Serenius and Stalder, 2007;
Sobczyńska et al., 2013) who reported that gilts that started
farrowing at younger ages had improved LPL and lifetime
production traits. Segura-Correa et al. (2011) indicated that
age at first farrowing could be reduced to 330 d and that
gilts in an early farrowing program should receive a higher
level of nutrition to ensure that they reach puberty at an
optimal body weight. Thus, age at first farrowing could be
considered an early indicator for LPL and lifetime
production traits that could be used to select sows for higher
LPL and lifetime productivity.
Yorkshire sows had longer LPL (739.84±16.10 d vs
675.01±12.06 d; p = 0.0008), higher LBA (53.11±1.14
piglets vs 50.14±0.86 piglets; p = 0.0314), higher LPW
(47.57±1.02 piglets vs 43.99±0.76 piglets; p = 0.0035),
heavier LBW (80.57±1.81 kg vs 76.02±1.36 kg; p = 0.0373)
and heavier LWW (329.42±7.65 kg vs 308.26±5.74 kg; p =
0.0163) than L sows in this commercial swine population
(Table 3). In contrast, Keonouchanh (2002) found non-
significant differences between L and Y sows for LPL, LBA,
and LPW. Values of LSM for LPL for Y and L sows here
were higher than mean values reported for these breeds in
various populations located in temperate regions (489 to
652 d for Y and 493 to 617 d for L; Yazdi et al., 2000a, b;
Serenius et al., 2008; Hoge and Bates, 2011; Sobczyńska et
al., 2013). The longer LPL for Y sows in this study
indicated that if sows were selected based on LPL and
lifetime production trait performance of their relatives, a
larger number of Y than L sows would likely be chosen as
replacements.
Genetic variances
Genetic, environmental, and phenotypic variance
components for LPL, LBA, LPW, LBW, and LWW in this
commercial population are shown in Table 4. Estimates of
animal genetic variances for these traits were higher than
values reported for Northeastern Thailand (Keonouchanh,
2002), and represented between 17% and 20% of the
phenotypic variances estimated for these traits (Table 4).
These levels of additive genetic variation indicated that
these LPL and lifetime production traits would respond to
genetic selection in this commercial swine population.
Table 4. Estimates of variance components for length of productive life and lifetime production traits
Traits Variance components1
σa2 σe
2 σp2
LPL (d2) 27,889.70±6,267.35 138,945.00±6,158.91 166,800.00±4,471.00
LBA (piglets2) 138.09±30.69 697.43±30.39 835.50±22.33
LPW (piglets2) 136.69±26.54 530.75±24.79 667.40±18.11
LBW (kg2) 323.35±73.82 1,771.88±75.02 2,095.00±55.78
LWW (kg2) 7,333.31±1,432.29 30,308.40±1,368.94 37,640.00±1,016.00
LPL, length of productive life; LBA, lifetime number of piglets born alive; LPW, lifetime number of piglets weaned; LBW, lifetime litter birth weight;
LWW, lifetime litter weaning weight.
1 σa
2, additive genetic variance; σ
e
2, environmental variance; σp
2, phenotypic variance.
Table 3. Least squares means and SE per breed group for length
of productive life and lifetime production traits
Traits Breed group
Landrace Yorkshire
Length of productive life (d) 675.02±12.06b 739.84±16.10a
Lifetime number of
piglets born alive (piglets)
50.14±0.86b 53.11±1.14a
Lifetime number of
piglets weaned (piglets)
43.99±0.76b 47.57±1.02a
Lifetime litter birth weight (kg) 76.02±1.36b 80.57±1.81a
Lifetime litter weaning weight
(kg)
308.26±5.74b 329.42±7.65a
SE, standard error. a,b Least squares means within a row with different superscript letters
differ (p<0.05).
Table 2. Levels of significance for factors included in the single-trait fixed model
Factors Traits
LPL LBA LPW LBW LWW
First farrowing year-season <0.0001 <0.0001 <0.0001 <0.0001 <0.0001
Breed group 0.0008 0.0314 0.0035 0.0373 0.0219
Age at first farrowing (d) <0.0001 <0.0001 <0.0001 0.0009 0.0007
LPL, length of productive life; LBA, lifetime number of piglets born alive; LPW, lifetime number of piglets weaned; LBW, lifetime litter birth weight;
LWW, lifetime litter weaning weight.
Noppibool et al. (0000) Asian Australas. J. Anim. Sci. 00:0000-0000
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Heritabilities
Estimates of heritabilities and their standard error for all
traits are presented in Table 5. The heritabilities were
0.17±0.04 for LPL, 0.17±0.04 for LBA, 0.20±0.04 for LPW,
0.15±0.03 for LBW and 0.19±0.04 for LWW. Heritabilities
estimated here were within the range of estimates reported
in previous studies (Lόpez-Serrano et al., 2000; Serenius
and Stalder, 2004; Serenius et al., 2008; Sobczyńska et al.,
2013), and they were higher than those estimated in a swine
population composed of Duroc, Landrace, and Large White
sows in Northeastern Thailand (Keonouchanh, 2002). The
medium size heritabilities obtained here for LPL, LBA,
LPW, LBW, and LWW indicate that these traits could be
integrated into a selection program to improve lifetime
production efficiency in this population. However, because
these are traits measured at the end of the productive life of
sows, LPL and lifetime production records will only be
useful to select future replacement sires and dams. Thus,
commercial producers could implement a genetic evaluation
and selection strategy that combined information on
production traits from early farrowings (e.g., first or first
and second) from young animals, and LPL and lifetime
production traits from animals that finished their productive
life to choose sow and boar replacements.
Genetic and phenotypic correlations
Estimates of genetic and phenotypic correlations
between LPL and lifetime production traits are shown in
Table 5. All estimates of genetic and phenotypic
correlations among these traits were high and positive
(greater than 0.92). These genetic and phenotypic
correlation estimates were in agreement with previously
reported values for LPL and lifetime production traits
(Serenius and Stalder, 2004; Sevón-Aimonen and Uimari,
2013; Sobczyńska et al., 2013), and were substantially
higher than results from Northeastern Thailand
(Keonouchanh, 2002), where genetic correlations between
LPL and lifetime production traits ranged from 0.37 to 0.65
in one farm and –0.14 to 0.25 in a second farm.
Positive correlations here indicated that sows with
higher LBA, higher LPW, heavier LBW and heavier LWW
tended to have a higher probability to remain longer in the
breeding herd. Selecting young boars and gilts for high
EBV values of LPL and lifetime production traits will in
turn positively influence the profitability of swine
operations by increasing revenues from higher EBV sows
and reducing boar and gilt replacement costs. Cost
reduction would be achieved by preselecting gilts and
young boars early in life using LPL and lifetime production
records from relatives, thus reducing the number of
candidates needed for replacement and the replacement
costs. Sows that have higher production efficiency and
longer LPL are likely to be more fertile, have more piglets
alive at birth and heavier litters at weaning over their
lifetime, thus increasing the profitability of the business
(Sasaki and Koketsu, 2008). In addition, sows that have
higher productivity and remain longer in the breeding herd
will also likely be healthier than sows that have shorter herd
life (Tummaruk et al., 2001).
The high genetic correlations among LPL and lifetime
production traits obtained here ensure that selection for
lifetime production traits will result in indirect improvement
of LPL because sires and dams with higher EBV for
lifetime production traits will also tend to have higher
progeny means for LPL. As indicated above, computing
preliminary EBV for gilts and young boars using records
from relatives would be a good tool to choose a smaller
group of superior young animals before sending them to the
breeding unit. This strategy would help keep a consistent
intensity of selection on these traits, stabilize genetic trends,
and reduce replacement costs.
Genetic trends
Mean yearly EBV for sows, sires, and dams for LPL,
LBA and LBW between 1989 and 2013 are shown in Figure
1 to 3. Figures of genetic trends for LPW and LWW (data
Table 5. Heritability (±SE; diagonal), phenotypic (±SE; below
diagonal), and genetic correlation (±SE; above diagonal)
estimates between length of productive life and lifetime
production traits
Traits LPL LBA LPW LBW LWW
LPL 0.17±0.04 0.96±0.02 0.99±0.02 0.94±0.02 0.93±0.02
LBA 0.94±0.00 0.17±0.04 0.98±0.01 0.97±0.01 0.95±0.01
LPW 0.95±0.00 0.95±0.00 0.20±0.04 0.96±0.01 0.98±0.01
LBW 0.93±0.00 0.97±0.00 0.94±0.00 0.15±0.03 0.97±0.01
LWW 0.93±0.00 0.93±0.00 0.98±0.00 0.94±0.00 0.19±0.04
SE, standard error; LPL, length of productive life; LBA, lifetime number
of piglets born alive; LPW, lifetime number of piglets weaned; LBW,
lifetime litter birth weight; LWW, lifetime litter weaning weight.
Figure 1. Genetic yearly means of sow, sire, and dam estimated
breeding values for length of productive life (LPL) from 1989 to
2013.
Noppibool et al. (0000) Asian Australas. J. Anim. Sci. 00:0000-0000
ED15-0647
6
not shown) were similar to those for lifetime litter traits at
birth. Dam genetic trends were positive and significant for
all traits (p<0.0001 to p<0.0217), whereas sire genetic
trends were negative for LPL and all lifetime production
traits (p = 0.0007 to p = 0.0487; Table 6). Thus, it appears
that sows were more consistently chosen based on number
of piglets born alive and litter weight at birth or at weaning,
whereas sires may have been chosen for other traits such as
growth. Sow genetic trends were small and non-significant
for all traits resulting from the positive genetic trends for
their dams and the negative genetic trends for their sires
(Table 6). The EBV yearly means for all traits tended to
decrease between 1989 and 2001 for sows, sires, and dams.
After 2001 the pattern of EBV yearly means differed in
sows, sires, and dams. Sire EBV yearly means decreased
from 2001 to 2006 then they decreased until 2013.
Conversely, dam EBV yearly means continued to increase
for LPL and life production traits until 2012 then they
dropped in 2013. Sow EBV yearly means also increased
from 2001 to 2012, then they dropped in 2013 to their
previous levels in 2011, showing values intermediate
between those of dams and sires. The smaller differences
between sire and dam mean EBV for lifetime production
traits from 1989 to 2001 indicated that sires may have been
chosen based on phenotypic records for production traits
(i.e., number of piglets born alive and litter weight at birth
or at weaning) of their dams and perhaps some close
relatives (e.g., sisters, aunts) during those years. Conversely,
the larger differences between sire and dam EBV from 2002
to 2013 indicated that sires may have been chosen for traits
other than number of piglets born alive and litter weight at
birth or at weaning. Although precise information on the
boar selection strategy in this population was unavailable, if
young boars were preferentially chosen based on own
growth performance during those years, bigger boars from
smaller litters (and lower EBV for LPL and lifetime
production traits) may have been chosen in larger numbers
than smaller boars from larger litters (and higher EBV for
LPL and lifetime production traits). Thus, if the primary
selection goal in this commercial population were to
improve LPL and lifetime production traits, then the sire
selection strategy would need to incorporate lifetime
production trait information and be consistent across years
to avoid sudden drops in yearly mean EBV. As suggested
above, a selection program that includes preselection of
young boars based on LPL, lifetime production records of
close relatives as well as production records from younger
female relatives could be implemented. Such multiple-trait
evaluation and selection program would help steadily
increase the EBV yearly means for sows, sires, and dams in
Table 6. Genetic trends for LPL and lifetime production traits for sows, sires, and dams
Animals Traits
LPL (d/yr) LBA (piglets/yr) LPW (piglets/yr) LBW (kg/yr) LWW (kg/yr)
Sows –0.77±0.45
(p = 0.1035)
0.02±0.03
(p = 0.5654)
–0.01±0.03
(p = 0.7216)
0.08±0.04
(p = 0.0975)
0.26±0.21
(p = 0.2243)
Sires –2.54±0.65
(p = 0.0007)
–0.12±0.04
(p = 0.0073)
–0.14±0.04
(p = 0.0037)
–0.13±0.06
(p = 0.0487)
–0.69±0.31
(p = 0.0365)
Dams 1.04±0.42
(p = 0.0217)
0.16±0.03
(p<0.0001)
0.12±0.03
(p = 0.0002)
0.29±0.04
(p<0.0001)
1.23±0.19
(p<0.0001)
LPL, length of productive life; LBA, lifetime number of piglets born alive; LPW, lifetime number of piglets weaned; LBW, lifetime litter birth weight;
LWW, lifetime litter weaning weight.
Figure 2. Genetic yearly means of sow, sire, and dam estimated
breeding values for lifetime number of piglets born alive (LBA)
from 1989 to 2013.
Figure 3. Genetic yearly means of sow, sire, and dam estimated
breeding values for lifetime litter birth weight (LBW) from 1989
to 2013.
Noppibool et al. (0000) Asian Australas. J. Anim. Sci. 00:0000-0000
ED15-0647
7
this commercial population.
CONCLUSION
The medium heritabilities for LPL, LBA, LPW, LBW,
and LWW indicated that genetic improvement for all these
traits would be feasible in this herd. The high and positive
genetic correlations between LPL and lifetime production
traits indicated that preliminary EBV for gilts and boars
using records from relatives could be used to preselect
young animals to improve LPL, LBA, LPW, LBW, and
LWW. Improvement of LPL and lifetime production traits
would be expected to lower gilt and boar replacement costs
as well as increase production efficiency and profitability of
this swine operation.
CONFLICT OF INTEREST
We certify that there is no conflict of interest with any
financial organization regarding the material discussed in
the manuscript.
ACKNOWLEDGMENTS
The authors are thankful for the financial support from
the Royal Golden Jubilee project (PHD/0230/2553) of the
Thailand Research Fund (TRF), the University of Florida
for supporting the training of the first author as a research
scholar, and the Four T Co., Ltd for providing the
phenotypic information for this research.
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